activity
20222025
most citedNICEST: Noisy Label Correction and Training for Robust Scene Graph Generation

5 citations · 18 across the 7 of their papers we have counts for

collaborators

7 papers

cs.CV2025

CoMo: Compositional Motion Customization for Text-to-Video Generation

Youcan Xu, Zhen Wang, Jiaxin Shi +6

While recent text-to-video models excel at generating diverse scenes, they struggle with precise motion control, particularly for complex, multi-subject motions. Although methods f…

cs.CV2023★ 4 cited

Compositional Feature Augmentation for Unbiased Scene Graph Generation

Lin Li, Guikun Chen, Jun Xiao +3

Scene Graph Generation (SGG) aims to detect all the visual relation triplets \texttt{sub}, \texttt{pred}, \texttt{obj} in a given image. With the emergence of various advance…

cs.CV2023★ 1 cited

Decomposed Prototype Learning for Few-Shot Scene Graph Generation

Xingchen Li, Jun Xiao, Guikun Chen +4

Today's scene graph generation (SGG) models typically require abundant manual annotations to learn new predicate types. Therefore, it is difficult to apply them to real-world appli…

cs.CV2022★ 3 cited

Label Semantic Knowledge Distillation for Unbiased Scene Graph Generation

Lin Li, Long Chen, Hanrong Shi +4

The Scene Graph Generation (SGG) task aims to detect all the objects and their pairwise visual relationships in a given image. Although SGG has achieved remarkable progress over th…

cs.CV2022

Integrating Object-aware and Interaction-aware Knowledge for Weakly Supervised Scene Graph Generation

Xingchen Li, Long Chen, Wenbo Ma +2

Recently, increasing efforts have been focused on Weakly Supervised Scene Graph Generation (WSSGG). The mainstream solution for WSSGG typically follows the same pipeline: they firs…

cs.CV2022★ 5 cited

NICEST: Noisy Label Correction and Training for Robust Scene Graph Generation

Lin Li, Jun Xiao, Hanrong Shi +4

Nearly all existing scene graph generation (SGG) models have overlooked the ground-truth annotation qualities of mainstream SGG datasets, i.e., they assume: 1) all the manually ann…

cs.CV2022★ 5 cited

Rethinking Multi-Modal Alignment in Video Question Answering from Feature and Sample Perspectives

Shaoning Xiao, Long Chen, Kaifeng Gao +4

Reasoning about causal and temporal event relations in videos is a new destination of Video Question Answering (VideoQA).The major stumbling block to achieve this purpose is the se…